Ishank graduated from USICT, GGS Indraprastha University in June 2016 with Electronics and Communication major. He was interested in doing research work in neural networks in association with speech processing/computer vision. "Professor Sajed's experience of academia and Ajay's genuine insights on admission process enabled me to secure several admits. Personal guidance sessions with my advisors and their prompt response to my queries benefited me in universities selection, admission essays evaluation, and post-admission preparations.Thank you, Stoodnt team, in guiding me through various stages of my graduate school applications!" - Ishank Sharma

Probabilistic mixture models have been widely used for different machine learning and pattern recognition tasks such as clustering, dimensionality reduction, and classification. In this paper, we focus on trying to solve the most common challenges related to supervised learning algorithms by using mixture probability distribution functions. With this modeling strategy, we identify sub-labels and generate synthetic data in order to reach better classification accuracy. It means we focus on increasing the training data synthetically to increase the classification accuracy.

The battle for artificial-intelligence expertise is forcing Apple Inc. to grapple with its famous penchant for secrecy, as tech companies seek to woo talent in a discipline known for its openness. The technology giant this year has been trying to draw attention--but only so much--to its efforts to develop artificial intelligence, or AI, a term that generally describes software that enables computers to learn and improve functions on their own. Apple launched a public blog in July to talk about its work, for example, and has allowed its researchers to speak at several conferences on artificial intelligence, including a TED Talk in April by Tom Gruber, co-creator of Apple's Siri voice assistant, that was posted on YouTube last month. Talking up transparency is unusual for a company whose chief executive, Tim Cook, once joked that it is more secretive than the Central Intelligence Agency. The shift is driven by AI's growing importance in areas like self-driving cars and voice assistants such as Siri.

Machine learning gives you unimaginably powerful insights into data. Today, implementations of machine learning have been adopted throughout Industry and its concepts are numerous. This video is a unique blend of projects that teach you what Machine Learning is all about and how you can implement machine learning concepts in practice. Six different independent projects will help you master machine learning in Python. The video will cover concepts such as classification, regression, clustering, and more, all the while working with different kinds of databases.

Not only do they give us a glimpse into some of the greatest minds around, they're inspiring and thought-provoking. Artificial Intelligence is a hot topic and TED have hosted some brilliant talks from people in the world of Artificial Intelligence, take a look below at our top 5. AI expert Stuart Russell shares his vision for human and AI compatibility, and how by using human values and attributes, Artificial Intelligence can solve problems. MIT grad student Joy Buolamwini explores the hot topic of bias in machine learning and her experiences with facial analysis software. This is an eye-opening talk not to be missed! Do you fear super intelligent AI?

What I learned: Key characteristics of big data and how to identify these with case samples. How this applies to my job: The course helped me have a better understanding of how to interpret data we pull for monthly social media metrics reports.

The following is an excerpt from Max Tegmark's new book Life 3.0: Being Human in the Age of Artificial Intelligence. What does it mean to be human in the present day and age? For example, what is it that we really value about ourselves, that makes us different from other life forms and machines? What do other people value about us that makes some of them willing to offer us jobs? Whatever our answers are to these questions at any one time, it's clear that the rise of technology must gradually change them.

In this course we are going to look at advanced NLP. Previously, you learned about some of the basics, like how many NLP problems are just regular machine learning and data science problems in disguise, and simple, practical methods like bag-of-words and term-document matrices. These allowed us to do some pretty cool things, like detect spam emails, write poetry, spin articles, and group together similar words. In this course I'm going to show you how to do even more awesome things. We'll learn not just 1, but 4 new architectures in this course.

This course is the next logical step in my deep learning, data science, and machine learning series. I've done a lot of courses about deep learning, and I just released a course about unsupervised learning, where I talked about clustering and density estimation. So what do you get when you put these 2 together? In these course we'll start with some very basic stuff - principal components analysis (PCA), and a popular nonlinear dimensionality reduction technique known as t-SNE (t-distributed stochastic neighbor embedding). Next, we'll look at a special type of unsupervised neural network called the autoencoder.